中山大学学报（自然科学版）2012，Vol.51Issue(6)：10-16,7.
基于支持向量机的不平衡数据分类的改进欠采样方法
An Improved SVM Based Under-Sampling Method for Classifying Imbalanced Data
摘要
Abstract
As a supervised classifier, Support Vector Machine ( SVM) has prominent advantages in solving some problems on petty and nonlinear datasets, but it is unsatisfying in tackling with imbalanced datasets. Random under-sampling has been a widely used method to improve SVM's performance on imbalanced data, but its stability is easily influenced by the nature of randomness. A modified SVM based on under-sampling method is presented to classify imbalanced data. Compared with the random under-sampling technique, it is shown through experiments on natural datasets that the new proposed under-sampling method is more stable in classifying imbalanced data, and exhibits improved SVM performance in classifying imbalanced data for many cases.关键词
支持向量机/不平衡数据/欠采样/稳定性Key words
support vector machine/ imbalanced data/ under-sampling/ stability分类
信息技术与安全科学引用本文复制引用
赵自翔,王广亮,李晓东..基于支持向量机的不平衡数据分类的改进欠采样方法[J].中山大学学报（自然科学版）,2012,51(6):10-16,7.基金项目
国家自然科学基金资助项目(U1135005) （U1135005）
